Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.9 KiB
Average record size in memory244.2 B

Variable types

Categorical6
Numeric10

Dataset

DescriptionEste es un analisis preeliminar para comprender de mejor forma los datos de nuestro dataset
AuthorKenneth David Leonel Triana , Juan Jose Naranjo, Alejandro Mora
URLhttps://github.com/kennethLeonel/Monografia-calidad-del-aire-valle-de-aburra

Alerts

anio has constant value "2024"Constant
festivo is highly imbalanced (71.4%)Imbalance
codigoserial is uniformly distributedUniform
estacion is uniformly distributedUniform
presion has 57 (31.7%) zerosZeros
p1 has 63 (35.0%) zerosZeros

Reproduction

Analysis started2024-10-15 00:06:31.066005
Analysis finished2024-10-15 00:06:49.696649
Duration18.63 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

anio
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
2024
180 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 180
100.0%

Length

2024-10-14T19:06:49.824015image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T19:06:49.981671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2024 180
100.0%

Most occurring characters

ValueCountFrequency (%)
2 360
50.0%
0 180
25.0%
4 180
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 360
50.0%
0 180
25.0%
4 180
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 360
50.0%
0 180
25.0%
4 180
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 360
50.0%
0 180
25.0%
4 180
25.0%

mes
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
93 
2
87 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Length

2024-10-14T19:06:50.149494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T19:06:50.321543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring characters

ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 93
51.7%
2 87
48.3%

dia
Real number (ℝ)

Distinct31
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.516667
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-10-14T19:06:50.566625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15.5
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7084849
Coefficient of variation (CV)0.56123426
Kurtosis-1.1864689
Mean15.516667
Median Absolute Deviation (MAD)7.5
Skewness0.011522316
Sum2793
Variance75.837709
MonotonicityNot monotonic
2024-10-14T19:06:50.798116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 6
 
3.3%
2 6
 
3.3%
29 6
 
3.3%
28 6
 
3.3%
27 6
 
3.3%
26 6
 
3.3%
25 6
 
3.3%
24 6
 
3.3%
23 6
 
3.3%
22 6
 
3.3%
Other values (21) 120
66.7%
ValueCountFrequency (%)
1 6
3.3%
2 6
3.3%
3 6
3.3%
4 6
3.3%
5 6
3.3%
6 6
3.3%
7 6
3.3%
8 6
3.3%
9 6
3.3%
10 6
3.3%
ValueCountFrequency (%)
31 3
1.7%
30 3
1.7%
29 6
3.3%
28 6
3.3%
27 6
3.3%
26 6
3.3%
25 6
3.3%
24 6
3.3%
23 6
3.3%
22 6
3.3%

pm25
Real number (ℝ)

Distinct179
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398.91051
Minimum-1235.5337
Maximum16692.348
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)5.6%
Memory size2.8 KiB
2024-10-14T19:06:51.006396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1235.5337
5-th percentile-389.33758
Q121.115891
median29.484673
Q336.236144
95-th percentile4185.2472
Maximum16692.348
Range17927.882
Interquartile range (IQR)15.120253

Descriptive statistics

Standard deviation1867.3281
Coefficient of variation (CV)4.6810701
Kurtosis42.74711
Mean398.91051
Median Absolute Deviation (MAD)7.6909042
Skewness5.9999492
Sum71803.892
Variance3486914.1
MonotonicityNot monotonic
2024-10-14T19:06:51.223735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.04166667 2
 
1.1%
20.46693333 1
 
0.6%
-395.235265 1
 
0.6%
19.12517083 1
 
0.6%
-814.26774 1
 
0.6%
22.30719125 1
 
0.6%
23.05252042 1
 
0.6%
26.29606667 1
 
0.6%
26.01087083 1
 
0.6%
18.8345425 1
 
0.6%
Other values (169) 169
93.9%
ValueCountFrequency (%)
-1235.533741 1
0.6%
-1234.117461 1
0.6%
-821.8938267 1
0.6%
-820.9419133 1
0.6%
-814.26774 1
0.6%
-402.1138379 1
0.6%
-401.103565 1
0.6%
-395.235265 1
0.6%
-391.8376 1
0.6%
-389.2060042 1
0.6%
ValueCountFrequency (%)
16692.34784 1
0.6%
12510.25325 1
0.6%
7524.074892 1
0.6%
4206.857683 1
0.6%
4202.691975 1
0.6%
4197.800408 1
0.6%
4197.21365 1
0.6%
4195.172246 1
0.6%
4188.727058 1
0.6%
4185.064037 1
0.6%

codigoserial
Categorical

UNIFORM 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
28
60 
69
60 
86
60 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters360
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28
2nd row28
3rd row28
4th row28
5th row28

Common Values

ValueCountFrequency (%)
28 60
33.3%
69 60
33.3%
86 60
33.3%

Length

2024-10-14T19:06:51.431427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T19:06:51.600479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
28 60
33.3%
69 60
33.3%
86 60
33.3%

Most occurring characters

ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 120
33.3%
6 120
33.3%
2 60
16.7%
9 60
16.7%

dia_semana
Categorical

Distinct7
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
Jueves
27 
Viernes
27 
Sabado
27 
Domingo
27 
Lunes
24 
Other values (2)
48 

Length

Max length9
Median length7
Mean length6.5666667
Min length5

Characters and Unicode

Total characters1182
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJueves
2nd rowViernes
3rd rowSabado
4th rowDomingo
5th rowLunes

Common Values

ValueCountFrequency (%)
Jueves 27
15.0%
Viernes 27
15.0%
Sabado 27
15.0%
Domingo 27
15.0%
Lunes 24
13.3%
Martes 24
13.3%
Miercoles 24
13.3%

Length

2024-10-14T19:06:51.793277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T19:06:51.996091image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
jueves 27
15.0%
viernes 27
15.0%
sabado 27
15.0%
domingo 27
15.0%
lunes 24
13.3%
martes 24
13.3%
miercoles 24
13.3%

Most occurring characters

ValueCountFrequency (%)
e 204
17.3%
s 126
10.7%
o 105
 
8.9%
i 78
 
6.6%
n 78
 
6.6%
a 78
 
6.6%
r 75
 
6.3%
u 51
 
4.3%
M 48
 
4.1%
J 27
 
2.3%
Other values (12) 312
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 204
17.3%
s 126
10.7%
o 105
 
8.9%
i 78
 
6.6%
n 78
 
6.6%
a 78
 
6.6%
r 75
 
6.3%
u 51
 
4.3%
M 48
 
4.1%
J 27
 
2.3%
Other values (12) 312
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 204
17.3%
s 126
10.7%
o 105
 
8.9%
i 78
 
6.6%
n 78
 
6.6%
a 78
 
6.6%
r 75
 
6.3%
u 51
 
4.3%
M 48
 
4.1%
J 27
 
2.3%
Other values (12) 312
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 204
17.3%
s 126
10.7%
o 105
 
8.9%
i 78
 
6.6%
n 78
 
6.6%
a 78
 
6.6%
r 75
 
6.3%
u 51
 
4.3%
M 48
 
4.1%
J 27
 
2.3%
Other values (12) 312
26.4%

estacion
Categorical

UNIFORM 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size14.2 KiB
Estacion Itagui
60 
Estacion Caldas
60 
Estacion Aranjuez
60 

Length

Max length17
Median length15
Mean length15.666667
Min length15

Characters and Unicode

Total characters2820
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstacion Itagui
2nd rowEstacion Itagui
3rd rowEstacion Itagui
4th rowEstacion Itagui
5th rowEstacion Itagui

Common Values

ValueCountFrequency (%)
Estacion Itagui 60
33.3%
Estacion Caldas 60
33.3%
Estacion Aranjuez 60
33.3%

Length

2024-10-14T19:06:52.235827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T19:06:52.416240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
estacion 180
50.0%
itagui 60
 
16.7%
caldas 60
 
16.7%
aranjuez 60
 
16.7%

Most occurring characters

ValueCountFrequency (%)
a 420
14.9%
t 240
 
8.5%
i 240
 
8.5%
n 240
 
8.5%
s 240
 
8.5%
E 180
 
6.4%
c 180
 
6.4%
o 180
 
6.4%
180
 
6.4%
u 120
 
4.3%
Other values (10) 600
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 420
14.9%
t 240
 
8.5%
i 240
 
8.5%
n 240
 
8.5%
s 240
 
8.5%
E 180
 
6.4%
c 180
 
6.4%
o 180
 
6.4%
180
 
6.4%
u 120
 
4.3%
Other values (10) 600
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 420
14.9%
t 240
 
8.5%
i 240
 
8.5%
n 240
 
8.5%
s 240
 
8.5%
E 180
 
6.4%
c 180
 
6.4%
o 180
 
6.4%
180
 
6.4%
u 120
 
4.3%
Other values (10) 600
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 420
14.9%
t 240
 
8.5%
i 240
 
8.5%
n 240
 
8.5%
s 240
 
8.5%
E 180
 
6.4%
c 180
 
6.4%
o 180
 
6.4%
180
 
6.4%
u 120
 
4.3%
Other values (10) 600
21.3%

festivo
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
171 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 171
95.0%
1 9
 
5.0%

Length

2024-10-14T19:06:52.598482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T19:06:52.758432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 171
95.0%
1 9
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 171
95.0%
1 9
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 171
95.0%
1 9
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 171
95.0%
1 9
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 171
95.0%
1 9
 
5.0%

temperatura
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.432939
Minimum-999
Maximum25.805556
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)7.2%
Memory size2.8 KiB
2024-10-14T19:06:52.942255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q121.149672
median22.569056
Q324.01053
95-th percentile25.146597
Maximum25.805556
Range1024.8056
Interquartile range (IQR)2.8608577

Descriptive statistics

Standard deviation236.97162
Coefficient of variation (CV)-6.3305643
Kurtosis12.689623
Mean-37.432939
Median Absolute Deviation (MAD)1.4451632
Skewness-3.7976194
Sum-6737.929
Variance56155.551
MonotonicityNot monotonic
2024-10-14T19:06:53.162613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
21.20336811 1
 
0.6%
22.43326389 1
 
0.6%
-487.1159722 1
 
0.6%
21.16861111 1
 
0.6%
22.01006944 1
 
0.6%
21.27694444 1
 
0.6%
21.59708333 1
 
0.6%
21.754375 1
 
0.6%
22.10930556 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-487.1159722 1
 
0.6%
-30.52168743 1
 
0.6%
-2.906805556 1
 
0.6%
13.58625 1
 
0.6%
17.50569444 1
 
0.6%
17.86701389 1
 
0.6%
18.04083333 1
 
0.6%
18.73861111 1
 
0.6%
18.8175 1
 
0.6%
ValueCountFrequency (%)
25.80555556 1
0.6%
25.52388889 1
0.6%
25.51756944 1
0.6%
25.50707636 1
0.6%
25.49993056 1
0.6%
25.49090278 1
0.6%
25.42810416 1
0.6%
25.39229167 1
0.6%
25.38145833 1
0.6%
25.13423608 1
0.6%

humedad
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7089607
Minimum-999
Maximum84.515444
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.1%
Memory size2.8 KiB
2024-10-14T19:06:53.374328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q160.754462
median67.10941
Q374.028226
95-th percentile79.726716
Maximum84.515444
Range1083.5154
Interquartile range (IQR)13.273764

Descriptive statistics

Standard deviation247.62645
Coefficient of variation (CV)43.375049
Kurtosis12.680085
Mean5.7089607
Median Absolute Deviation (MAD)6.7010069
Skewness-3.7943879
Sum1027.6129
Variance61318.859
MonotonicityNot monotonic
2024-10-14T19:06:53.590740image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
84.51544444 1
 
0.6%
74.615 1
 
0.6%
-457.6452083 1
 
0.6%
82.33111111 1
 
0.6%
77.93104167 1
 
0.6%
77.36708333 1
 
0.6%
74.94527778 1
 
0.6%
73.88020833 1
 
0.6%
71.14763889 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-457.6452083 1
 
0.6%
17.6589931 1
 
0.6%
31.39416667 1
 
0.6%
54.21333333 1
 
0.6%
54.94104167 1
 
0.6%
55.531875 1
 
0.6%
55.66118056 1
 
0.6%
56.40430556 1
 
0.6%
56.65270833 1
 
0.6%
ValueCountFrequency (%)
84.51544444 1
0.6%
84.4698125 1
0.6%
83.79554867 1
0.6%
83.44047228 1
0.6%
82.77643755 1
0.6%
82.33111111 1
0.6%
81.34544446 1
0.6%
80.49526392 1
0.6%
79.72923611 1
0.6%
79.7265833 1
0.6%

presion
Real number (ℝ)

ZEROS 

Distinct114
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.74671
Minimum-999
Maximum853.8609
Zeros57
Zeros (%)31.7%
Negative14
Negative (%)7.8%
Memory size2.8 KiB
2024-10-14T19:06:53.800324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median825.43719
Q3850.27476
95-th percentile852.5442
Maximum853.8609
Range1852.8609
Interquartile range (IQR)850.27476

Descriptive statistics

Standard deviation528.43086
Coefficient of variation (CV)1.1697503
Kurtosis0.60645694
Mean451.74671
Median Absolute Deviation (MAD)26.514375
Skewness-1.1449589
Sum81314.407
Variance279239.17
MonotonicityNot monotonic
2024-10-14T19:06:54.036215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
31.7%
-999 10
 
5.6%
-1.3875 2
 
1.1%
852.4464583 1
 
0.6%
850.2206944 1
 
0.6%
850.9357639 1
 
0.6%
851.6726389 1
 
0.6%
853.2250694 1
 
0.6%
853.8609028 1
 
0.6%
852.475625 1
 
0.6%
Other values (104) 104
57.8%
ValueCountFrequency (%)
-999 10
 
5.6%
-80.75256944 1
 
0.6%
-52.03125 1
 
0.6%
-1.3875 2
 
1.1%
0 57
31.7%
798.1502778 1
 
0.6%
814.9458333 1
 
0.6%
824.0225 1
 
0.6%
824.0748611 1
 
0.6%
824.114375 1
 
0.6%
ValueCountFrequency (%)
853.8609028 1
0.6%
853.2250694 1
0.6%
853.1511806 1
0.6%
853.0895139 1
0.6%
852.8140972 1
0.6%
852.7811806 1
0.6%
852.7346528 1
0.6%
852.681875 1
0.6%
852.6081944 1
0.6%
852.5408333 1
0.6%

p1
Real number (ℝ)

ZEROS 

Distinct50
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-116.92111
Minimum-999
Maximum0.064527778
Zeros63
Zeros (%)35.0%
Negative41
Negative (%)22.8%
Memory size2.8 KiB
2024-10-14T19:06:54.272326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median0
Q30.00014409722
95-th percentile0.017636806
Maximum0.064527778
Range999.06453
Interquartile range (IQR)0.00014409722

Descriptive statistics

Standard deviation317.05482
Coefficient of variation (CV)-2.7116987
Kurtosis3.9114326
Mean-116.92111
Median Absolute Deviation (MAD)5.2083333 × 10-5
Skewness-2.408165
Sum-21045.8
Variance100523.76
MonotonicityNot monotonic
2024-10-14T19:06:54.490283image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
35.0%
-999 20
 
11.1%
1.388888889 × 10-512
 
6.7%
6.944444444 × 10-610
 
5.6%
0.001791666667 3
 
1.7%
0.000125 3
 
1.7%
3.472222222 × 10-53
 
1.7%
0.009680555556 2
 
1.1%
0.002388888889 2
 
1.1%
0.007159722222 2
 
1.1%
Other values (40) 60
33.3%
ValueCountFrequency (%)
-999 20
11.1%
-496.725 2
 
1.1%
-27.75 1
 
0.6%
-6.243708333 2
 
1.1%
-6.242159722 1
 
0.6%
-5.549923611 1
 
0.6%
-4.1625 1
 
0.6%
-2.08125 1
 
0.6%
-2.081243056 1
 
0.6%
-2.079875 1
 
0.6%
ValueCountFrequency (%)
0.06452777778 2
1.1%
0.04209027778 2
1.1%
0.03158333333 2
1.1%
0.02457638889 1
0.6%
0.02313888889 2
1.1%
0.01734722222 2
1.1%
0.01594444444 1
0.6%
0.01265277778 2
1.1%
0.009680555556 2
1.1%
0.007159722222 2
1.1%

velocidad_prom
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-57.11957
Minimum-999
Maximum3.4436111
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)11.1%
Memory size2.8 KiB
2024-10-14T19:06:54.710655image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.5083333
median1.790691
Q32.0823125
95-th percentile2.5759441
Maximum3.4436111
Range1002.4436
Interquartile range (IQR)0.57397917

Descriptive statistics

Standard deviation232.09903
Coefficient of variation (CV)-4.0633889
Kurtosis12.694335
Mean-57.11957
Median Absolute Deviation (MAD)0.29175347
Skewness-3.798311
Sum-10281.523
Variance53869.959
MonotonicityNot monotonic
2024-10-14T19:06:54.942206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
1.911458333 1
 
0.6%
1.887013889 1
 
0.6%
-496.1823611 1
 
0.6%
1.655555556 1
 
0.6%
1.480625 1
 
0.6%
1.349027778 1
 
0.6%
0.776875 1
 
0.6%
1.023055556 1
 
0.6%
1.302777778 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-496.1823611 1
 
0.6%
-49.77319444 1
 
0.6%
-25.90798611 1
 
0.6%
-4.976111111 1
 
0.6%
-4.195416667 1
 
0.6%
-2.930625 1
 
0.6%
-2.375486111 1
 
0.6%
-0.23 1
 
0.6%
-0.211875 1
 
0.6%
ValueCountFrequency (%)
3.443611111 1
0.6%
3.122361111 1
0.6%
3.025694444 1
0.6%
2.753708333 1
0.6%
2.744375 1
0.6%
2.74 1
0.6%
2.687604167 1
0.6%
2.671770833 1
0.6%
2.582006944 1
0.6%
2.575625 1
0.6%

velocidad_max
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-56.02921
Minimum-999
Maximum4.9930556
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)9.4%
Memory size2.8 KiB
2024-10-14T19:06:55.158005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q12.3656771
median2.8410417
Q33.4249306
95-th percentile3.909375
Maximum4.9930556
Range1003.9931
Interquartile range (IQR)1.0592535

Descriptive statistics

Standard deviation232.37157
Coefficient of variation (CV)-4.147329
Kurtosis12.693508
Mean-56.02921
Median Absolute Deviation (MAD)0.55
Skewness-3.7982036
Sum-10085.258
Variance53996.545
MonotonicityNot monotonic
2024-10-14T19:06:55.391134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
2.690763889 1
 
0.6%
3.514513889 1
 
0.6%
-495.9194444 1
 
0.6%
3.048194444 1
 
0.6%
2.650833333 1
 
0.6%
2.422222222 1
 
0.6%
1.376180556 1
 
0.6%
1.782430556 1
 
0.6%
2.388888889 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-495.9194444 1
 
0.6%
-48.69340278 1
 
0.6%
-24.37902778 1
 
0.6%
-3.936736111 1
 
0.6%
-3.084027778 1
 
0.6%
-1.473333333 1
 
0.6%
-0.8536805556 1
 
0.6%
1.231666667 1
 
0.6%
1.272083333 1
 
0.6%
ValueCountFrequency (%)
4.993055556 1
0.6%
4.681458333 1
0.6%
4.515555556 1
0.6%
4.349027778 1
0.6%
4.18 1
0.6%
4.134583333 1
0.6%
4.075347222 1
0.6%
4.015138889 1
0.6%
3.927847222 1
0.6%
3.908402778 1
0.6%

direccion_prom
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.374823
Minimum-999
Maximum218.19236
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.1%
Memory size2.8 KiB
2024-10-14T19:06:55.610595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1109.08299
median136.2316
Q3159.21667
95-th percentile193.29299
Maximum218.19236
Range1217.1924
Interquartile range (IQR)50.133681

Descriptive statistics

Standard deviation265.70066
Coefficient of variation (CV)3.7755073
Kurtosis12.185079
Mean70.374823
Median Absolute Deviation (MAD)24.6125
Skewness-3.6911678
Sum12667.468
Variance70596.839
MonotonicityNot monotonic
2024-10-14T19:06:55.849365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
84.14166667 1
 
0.6%
173.2243056 1
 
0.6%
-419.5256944 1
 
0.6%
175.8034722 1
 
0.6%
208.6076389 1
 
0.6%
199.5472222 1
 
0.6%
185.8145833 1
 
0.6%
192.6840278 1
 
0.6%
191.6965278 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-419.5256944 1
 
0.6%
32.75416667 1
 
0.6%
42.34027778 1
 
0.6%
54.46111111 1
 
0.6%
57.62986111 1
 
0.6%
58.51388889 1
 
0.6%
66.35208333 1
 
0.6%
66.93194444 1
 
0.6%
66.96041667 1
 
0.6%
ValueCountFrequency (%)
218.1923611 1
0.6%
214.6555556 1
0.6%
208.6076389 1
0.6%
206.0354167 1
0.6%
204.1854167 1
0.6%
202.4833333 1
0.6%
199.5472222 1
0.6%
197.7840278 1
0.6%
195.4555556 1
0.6%
193.1791667 1
0.6%

direccion_max
Real number (ℝ)

Distinct171
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.356258
Minimum-999
Maximum202.66528
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)6.1%
Memory size2.8 KiB
2024-10-14T19:06:56.084777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1114.85885
median143.16875
Q3162.8
95-th percentile187.22639
Maximum202.66528
Range1201.6653
Interquartile range (IQR)47.941146

Descriptive statistics

Standard deviation266.46344
Coefficient of variation (CV)3.5836048
Kurtosis12.236136
Mean74.356258
Median Absolute Deviation (MAD)23.489583
Skewness-3.703159
Sum13384.126
Variance71002.767
MonotonicityNot monotonic
2024-10-14T19:06:56.302892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 10
 
5.6%
101.6451389 1
 
0.6%
170.4083333 1
 
0.6%
-419.0034722 1
 
0.6%
173.2416667 1
 
0.6%
201.39375 1
 
0.6%
200.6131944 1
 
0.6%
187.1798611 1
 
0.6%
189.20625 1
 
0.6%
188.1104167 1
 
0.6%
Other values (161) 161
89.4%
ValueCountFrequency (%)
-999 10
5.6%
-419.0034722 1
 
0.6%
34.58958333 1
 
0.6%
45.12152778 1
 
0.6%
58.35555556 1
 
0.6%
58.8625 1
 
0.6%
64.87361111 1
 
0.6%
67.05972222 1
 
0.6%
67.57013889 1
 
0.6%
67.90416667 1
 
0.6%
ValueCountFrequency (%)
202.6652778 1
0.6%
201.39375 1
0.6%
200.6131944 1
0.6%
198.9680556 1
0.6%
198.5291667 1
0.6%
197.2097222 1
0.6%
195.1395833 1
0.6%
189.20625 1
0.6%
188.1104167 1
0.6%
187.1798611 1
0.6%

Interactions

2024-10-14T19:06:47.644336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:32.539967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:34.174194image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:35.752786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:38.515304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.988197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:41.407518image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:42.912045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:44.381838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:45.856312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:47.804202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:32.709453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:34.321351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:35.914532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:38.668324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:40.129109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:41.558032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:43.059182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:44.538952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:46.006135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:47.966826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:32.879525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:34.466704image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:36.062908image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:38.833956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:40.276600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:41.727528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:43.221304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:44.710066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:46.184201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:48.096007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:33.036830image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:34.619080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:36.204117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.003736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:40.408835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:41.882923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:43.363674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:44.852140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:46.337983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:48.232602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:33.181723image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:34.765890image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:36.356095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.143985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:40.544073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:42.017515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:43.490540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:44.994402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:46.482718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:48.383754image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:33.352680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:34.919081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:36.510384image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.300324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:40.690556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:42.154783image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:43.643644image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:45.151309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:46.638545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:48.519263image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:33.516071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:35.078205image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:36.653774image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.442998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:40.824155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:42.296909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:43.773630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:45.297732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:47.074695image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:48.658335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:33.676786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:35.225706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:36.792422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.576103image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:40.964729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:42.445703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:43.920959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:45.428769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:47.206335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:48.791675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:33.838545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:35.381449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:36.928850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.718892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:41.101253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:42.603118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:44.055861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:45.563880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:47.359199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:48.926942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:34.009192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:35.528847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:37.065086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:39.856954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:41.249009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:42.759482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:44.221916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:45.715116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-14T19:06:47.491639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-10-14T19:06:49.165905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-14T19:06:49.564630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

aniomesdiapm25codigoserialdia_semanaestacionfestivotemperaturahumedadpresionp1velocidad_promvelocidad_maxdireccion_promdireccion_max
020242130.08333328JuevesEstacion Itagui021.20336884.5154440.00000.0315831.9114582.69076484.141667101.645139
120242236.04166728ViernesEstacion Itagui021.54819482.7764380.00000.0420901.5685832.275486158.501389172.629861
220242316.16666728SabadoEstacion Itagui020.82843183.7955490.00000.0231391.3688402.092778154.681944161.095833
320242423.45833328DomingoEstacion Itagui020.93809081.3454440.00000.0017921.1571251.656389162.447222177.250694
420242529.66666728LunesEstacion Itagui020.91945880.4952640.00000.0015211.2501532.090069189.303472195.139583
520242662.37500028MartesEstacion Itagui021.43740379.1733400.00000.0000001.5573892.386250160.106944173.673611
620242715.75000028MiercolesEstacion Itagui021.09285477.957590-1.3875-1.3874030.7385971.693264100.135417116.951389
72024288.20833328JuevesEstacion Itagui021.22233383.4404720.0000-6.2437082.6717713.75840382.16944493.245139
820242914.83333328ViernesEstacion Itagui022.63290378.1585000.00000.0000002.5756253.71819490.926389102.394444
9202421018.08333328SabadoEstacion Itagui023.94538973.5895690.00000.0000072.3104933.459514108.813194129.338194
aniomesdiapm25codigoserialdia_semanaestacionfestivotemperaturahumedadpresionp1velocidad_promvelocidad_maxdireccion_promdireccion_max
5020243223779.23561686ViernesEstacion Aranjuez023.66625059.241944848.580556-1.3875000.0375691.231667175.651389168.935417
5120243234202.69197586SabadoEstacion Aranjuez024.46652866.774931851.5370140.0000002.0129173.691806195.455556175.600000
5220243244197.21365086DomingoEstacion Aranjuez024.46972265.367500851.0305560.0000001.7116673.078056146.755556144.386111
53202432527.40206286LunesEstacion Aranjuez124.33840361.306250851.5416670.0000691.5615972.834583168.268056158.643056
5420243267524.07489286MartesEstacion Aranjuez023.39131958.621875850.758403-0.6937501.0609032.531111172.701389163.200000
55202432740.51306786MiercolesEstacion Aranjuez023.06111164.514722852.7811810.0000071.7534033.166944214.655556198.529167
56202432834.30334686JuevesEstacion Aranjuez124.02527863.482431852.8140970.0000001.9777083.635903175.940972171.405556
572024329-389.20600486ViernesEstacion Aranjuez123.45881965.010208852.4464580.0000351.9295833.559375174.241667173.286806
58202433033.91124686SabadoEstacion Aranjuez023.62916765.643403851.8540970.0000001.9527783.571667169.008333168.874306
59202433141.06332586DomingoEstacion Aranjuez021.60270878.103958852.6818750.0013331.4586812.623889139.196528142.368056